Method and system for recommending target object information

ABSTRACT

Embodiments of the present application relate to a method for recommending target object information, a system for recommending target object information, a client for recommending target object information, a server for recommending target object information, and a computer program product for recommending target object information. A method for recommending target object information is provided. The method includes receiving a target object informational recommendation request including information pertaining to a plurality of short-listed objects selected, determining historical selection information on the plurality of short-listed objects, the historical selection information including a historical count, a selection count, or both, and sending the part or all of the short-listed object historical selection information to a client.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to People's Republic of China PatentApplication No. 201210470206.1 entitled TARGET OBJECT INFORMATIONRECOMMENDING METHOD, SERVER, AND CLIENT, filed Nov. 20, 2012 which isincorporated herein by reference for all purposes.

FIELD OF THE INVENTION

The present invention relates to a method and system for recommendingtarget object information.

BACKGROUND OF THE INVENTION

Internet applications have become popular for individuals and businessesalike. Currently, there are many types of Internet business services,which are primarily deployed on various Internet servers. For example,in popular transaction-type websites, network servers provide variouskinds of transaction services to the public. The transaction servicesinclude physical entity transaction services, information transactionservices, etc. Having user display descriptive information on businessobjects which a website is able to provide on a web page so that otherusers may make their selections is an example in which a businessservice is implemented on such a website. For example, if the businessobject is a physical entity, the descriptive information displayed onthe web page includes images together with name, use, and price of thebusiness object. In another example, when a business object isinformation, descriptive information displayed on the web page is asummary of the information or key phrases. After the descriptiveinformation on the business objects, which a website is able to provideon a web page, is displayed by browsing the business objects displayedon the web page, other users are able to select the objects which theythemselves use for conducting subsequent business services. For example,a request is sent to the server to obtain a business object. The websiteserver acquires the object according to an established manner ofprocessing. A plurality of such established manners of processing mayexist. For example, the user requests login, or the user requests payinga certain fee for the object.

Presently, when a user selects a business object displayed on the webpage, the user's selection is based mainly on the descriptiveinformation on the selected business object. For example, the user viewsname, purpose, pictures, and other such information about the objectdisplayed on the web page, and thereby determines whether the businessobject complies with the user's requirements. In actual applications, alarge volume of business objects exists on the Internet. Typically, thedescriptive information for each business object is a simple descriptionof the business object. Often, it is very difficult for users to have afull understanding of the business object based on the descriptiveinformation of the business object. For example, it is very difficultfor a user who has not yet actually obtained a business object todetermine whether the business object described on a web page will haveoperating defects, whether the pictures of the business object areauthentic, whether the descriptive information is accurate, etc.Selecting the appropriate business object when false descriptiveinformation has been added to the business object is particularlydifficult for the user.

In some conventional Internet applications, a first user will, whilebrowsing business objects, establish a connection with a second user whomay understand the business objects browsed by the first user to givesome advice to determine whether the business objects are worth buying.For example, the first user may ask the second user who is a friend ofthe first user what is the second user's opinion about the businessobjects (for example, the products) which the first user wants to buy.

Some of the limitations described above are: 1) The second user may notactually understand the business objects browsed by the first user.Therefore, confirmation information describing the business objects thatare obtained from the second user may not be accurate. 2) Such a mode ofcommunication lacks focus and can waste server and network resources. 3)Even if the first user knows that the second user understands thebrowsed business objects, the second user may not stay online. In thiscase, the first user cannot promptly establish a connection with thesecond user to obtain a response and is thus unable to acquireinformation confirming the business object. This is inefficient. 4) Evenif the first user is able to establish a communication connection withthe second user, the confirmation information acquired may be verylimited. A very high likelihood exists that false descriptiveinformation is provided by the business object provider, and theconfirmation information cannot reflect the accuracy of the descriptiveinformation corresponding to the business object.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

In order to provide a clearer explanation of the technical solutions inthe present application, simple introductions are given below to thedrawings, which are needed to describe the embodiments or the prior art.Obviously, the drawings described below are merely some examples of thepresent invention. Persons with ordinary skill in the art could, withoutexpending creative effort, obtain other drawings on the basis of thesedrawings

FIG. 1 is a structural diagram of an embodiment of a system forrecommending target object information.

FIG. 2 is a flowchart of an embodiment of a method for recommendingtarget object information.

FIG. 3 is a structural diagram of an example of a system forrecommending target object information.

FIG. 4 is a schematic diagram of an example of a client browser displaystatus in a system for recommending target object information.

FIG. 5 is a flowchart of another embodiment of a method for recommendingtarget object information.

FIG. 6A is a flowchart of yet another embodiment of a method forrecommending target object information.

FIG. 6B is a flowchart of yet another embodiment of a method forrecommending target object information.

FIG. 7 is a flowchart of yet another embodiment of a method forrecommending target object information.

FIG. 8 is a flowchart of yet another embodiment of a method forrecommending target object information.

FIG. 9 is a flowchart of yet another embodiment of a method forrecommending target object information.

FIG. 10 is a structural diagram of an embodiment of a server forrecommending target object information.

FIG. 11 is a structural diagram of an embodiment of a client forrecommending target object information.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

The present application provides embodiments of a method and system forrecommending target object information. The method and system can beapplied to the process whereby users browse business objects on webpages. The method and system for recommending target object informationprovided by the present application can be applied to various kinds ofInternet business websites.

FIG. 1 is a structural diagram of an embodiment of a system forrecommending target object information. In some embodiments, the system100 includes a server 110 connected to a plurality of clients 120 via anetwork 130. The clients 120 include but are not limited to computers,smart phones, tablet computers, and other such hardware. Web pages andapplications are presented by such hardware. An operator or operators ofclient 120 are one or more online users.

FIG. 2 is a flowchart of an embodiment of a method for recommendingtarget object information. In some embodiments, the method 200 isimplemented by the server 110 of FIG. 1 and comprises:

In 210, the server receives a target object informational recommendationrequest. In some embodiments, the request includes information on aplurality of short-listed objects selected by a first user.

As an example, the first user visits a website through a client such asa browser, browses business objects displayed on corresponding webpages, and selects a business object to confirm as final target objects.For example, product objects displayed on the corresponding web pages ofthe website are business objects. Examples of the business objectsinclude physical products, virtual products, value information, etc. Thefinal target objects are confirmed by browsing the business objects onthe corresponding web pages and by selecting the business objects.

Since numerous business objects exist and the information on thebusiness objects displayed on the web pages is limited, in someembodiments, the first user finds selecting the final target objectswhile browsing the numerous business objects difficult due to thelimited information on the business objects. Therefore, in someembodiment, the first user selects a plurality of business objects asshort-listed objects (for example, objects placed in a shopping cart)based on the business objects displayed on the web pages.

After the first user has selected the plurality of short-listed objectson the client, and after the client generates a target objectrecommendation request, the client sends the target object informationalrecommendation request to the server that displays the business objects.In some embodiments, the target object informational recommendationrequest includes information on the short-listed objects selected by thefirst user. Examples of the information on the selected short-listedobjects includes one or more of the categories to which the short-listedobjects belong, names of the short-listed objects, item number, item ID,and other such information.

FIG. 3 is a structural diagram of an example of a system forrecommending target object information. The system 300 includes a server310 that serves as an online shopping platform, for example, Taobao,Tmall, etc. Users log onto a website through client 330 and visit theserver 310 of the commercial platform via a network 320. A user database350 and an object information database 340 are connected to the server310 and the client 330 via the network 320. The users select productsthat they wish to purchase from the products shown on the server 310.

FIG. 4 is a schematic diagram of an example of a client browser displaystatus in a system for recommending target object information. In someembodiments, the client browser display status is implemented on theclient 330 of FIG. 3. After user A, who corresponds to a first user,logs onto the e-commerce platform through a web page of the user'sclient 330, the first user selects from the products provided by theserver 310 of the commercial platform. Once user A has determined aplurality of short-listed objects for the same type of product, user Aselects one or more of the short-listed objects as products for finalpurchase and places an order. When presented with all the short-listedobjects, user A is faced with many choices. Therefore, user A would likethe server 310 to provide informational recommendations for productsthat are short-listed objects.

For example, the commercial platform server 310 provides an applicationthat solicits recommendations from many users. After user A browses theTaobao website, user A wishes to purchase a shoulder bag. After browsingthe Taobao website, user A selects 6 or 7 products as short-listedproducts. In some embodiments, the short-listed products are placed in avirtual shopping cart or wish list. However, user A finds choosingbetween the short-listed products very difficult. Therefore, the userselects an application such as the “My Taobao” interface in the browserof the client 330. After selecting the application, the user sends aninformational recommendation application request. The request server 310presents informational recommendations for the short-listed productswhich user A had selected. For example, first, the user selects productsrequiring recommendation and has the selected products serve asshort-listed objects. When selecting information-recommended products,user A selects browsed products from the browser records, selectsbookmarked products from the bookmark folder or selected products frompurchase records, or directly enter a product link in a prompt field.Through the above means, the user selects products for additionalinformational recommendations.

For example, referring back to FIG. 4, four products X, Y, Z and Qexist. User A would like platform server 310 to invite a plurality ofusers to help user A by giving user A informational recommendations onthe four products X, Y, Z and Q.

After user A selects the short-listed objects X, Y, Z and Q via abrowser interface of the client 330, user A clicks the interface controlon the client 320. The client 320 sends out a target objectinformational recommendation request.

Referring back to FIG. 2, in 220, the server determines historicalselection information on the plurality of short-listed objects, thehistorical selection information comprising an historical count, aselection count, or any combination thereof. In some embodiments, thehistorical count is the number of times in the past that eachshort-listed object among the plurality of short-listed objects wasselected by second users with the target object informationalrecommendation request being sent to a server. In some embodiments, theselection count is the number of times that the short-listed objectsamong target object informational recommendation requests (whosequantity is the historical count) were selected by the second users astarget objects. As used herein, the first user is a user browsing a webpage and preparing to select target objects. The second user is a userwho has previously selected target objects.

As an example, the server performs table look-ups based on thecharacteristic information (for example, the categories to which theshort-listed objects belong, names of the short-listed objects, itemnumber, item ID, etc.) on the short-listed objects included in thetarget object informational recommendation request sent by the firstuser. In other words, the server looks up and confirms the historicalselection information related to the short-listed objects in an objectinformation database 340.

In some embodiments, the historical selection information includes thenumber of times that the second users select short-listed objects andthe number of times that the second users select final target objects,such information being drawn from messages previously sent by secondusers to the server to acquire confirmation of target object informationand/or information confirmation activities with respect to one or moreof these short-listed objects. However, the historical information isnot limited to the above two types of “number of times.” In someembodiments, the historical information also includes evaluationinformation and other such information by the second user concerning theshort-listed objects. In some embodiments, the confirmation of thetarget object information relates to the second user confirming that thetarget object is worth purchasing. In some embodiments, the informationconfirmation activities relates to which objects are worth purchasingfrom among the plurality of users' objects. Examples of the evaluationinformation include a good review, a mixed review, a poor review, etc.

The server determines the historical selection information correspondingto each short-listed object of the first user in the object informationdatabase. For example, the historical selection information relates tothe number of times that each short-listed object has been selected as ashort-listed object by all second users who have engaged in sendingtarget object information confirmations, the number of times that finaltarget objects have been selected by second users from the number oftimes that short-listed objects were selected this time, and theevaluation information of second users on the short-listed objects.

Accordingly, in some embodiments, the server acquires the historicalselection information on the short-listed objects based on onlinecounting or offline counting. Regarding the acquisition of thehistorical selection information based on the online counting, each timea user sends a target object informational recommendation requestthrough a client, the server determines the historical selectioninformation relating to the short-listed objects. Regarding theacquisition of the historical selection information based on offlinecounting, the server automatically saves the number of times that eachshort-listed object is selected to participate in informationconfirmation activities and the number of times that the short-listedobject is selected as a final target object. Each time that the serverreceives a target object request sent by a new first user, the serverlooks up the data relating to the target objects in the objectinformation database.

There are many ways to generate an entry of the historical selectioninformation. For example, the server regards the number of times thatone of a plurality of short-listed objects was selected by the firstuser or second users as short-listed objects as the historical count inthe historical selection information. In addition, the server counts thenumber of times that the first user or the second users ultimatelyselect one short-listed object as the final target object as theselection count for the short-listed objects.

In another example, after the first user has selected a plurality ofshort-listed objects, the first user autonomously selects N (N being aninteger greater than 1) third users. For example, on a platformsupporting social networking functions, users may identify other usersas friends and request links to them (e.g., “follow” them) to establishsocial network connections. The social network connections are trackedby the server. Thus, the third user can be a friend of the first user,or the first user and the third user are following each other via theirsocial network connections. The first user sends the selectioninformation on the selected short-listed objects through the client tothe N third users. Each of the N third users selects one or moreshort-listed objects that the each third user supports. At this point,the short-listed objects are recommended by the N third users, whichcount as N times within the historical count. In some embodiments, thenumber of times that the short-listed objects are recommended by thethird user is added to the selection count in the historical selectioninformation. As an example, a recommendation indicates that in theshort-listed objects, a portion or all of the objects have beenrecommended by the third user.

In yet another example, after receiving a target object informationalrecommendation request, the server randomly selects N registered usersto serve as third users. The server sends the selection information onthe short-listed objects selected by the first user to the N thirdusers. Each third user selects one or more short-listed objects that theeach third user supports. At this point, the short-listed objects arerecommended to the N third users, which count as N times within thehistorical count. In some embodiments, the number of times that theshort-listed objects are supported by the third user is added to theselection count in the historical selection information. In other words,after product information of short-listed objects are sent to thirdusers, each third user will separately support or recommend certainproducts among the candidate products. This support or recommend issimilar to a vote where a plurality of third users vote for candidateproducts, and the number of votes can be used as the supportinformation.

In the above examples, the server not only records choice records andselection records, but the server also saves the time whenever eachshort-listed object is selected as a short-listed object, the time wheneach short-listed object is selected as a target object, or anycombination thereof, or the time when each short-listed object isrecommended by a third user.

Referring back to FIG. 3 as an example, after the server 310 receivesuser A's request, the server 310 then performs a table look-up or anindexing approach to the object information database based on thecontent of the request and the characteristic information of theshort-listed objects in order to determine the historical selectioninformation for each short-listed object X, Y, Z and Q. In other words,data exists such as the number of times that the short-listed objectswere selected to participate in informational recommendation activitiesand the number of times that the short-listed objects were selected,purchased, or a combination thereof. The number of times that theshort-listed objects were selected, purchased, or a combination thereofis a subset of the number of times that the short-listed objects wereselected to participate in the informational recommendation activities.

In 230, the server takes part of or all of the short-listed objecthistorical selection information from among the plurality ofshort-listed objects, or support information obtained by referencing thehistorical selection information, and sends the historical selectioninformation or the support information to the client.

As an example, the server looks up the historical count, i.e., thenumber of past times that each short-listed object was selected from theplurality of short-listed objects in order to participate in auxiliaryrequests and acquires the number of times that the short-listed objectswere selected from among the number of past times of auxiliary requeststo serve as the target product selections. In some embodiments, anauxiliary request allows the second user or the third user to assist thefirst user in selecting from the short-listed products. The servercalculates a support ratio for each short-listed object based on thehistorical count and the selection count, and the server sends thesupport ratio as support information to the client so that the firstuser that selected the short-listed objects confirms the short-listedobjects and acquires reliable data on the short-listed objects. Thesupport ratio relates to the number of times a short-listed object isselected divided by the total number of selections N.

In another example, the historical selection information is directlysent to the client. The client then calculates a support ratio from thehistorical count and the selection count of the historical selectioninformation.

For example, the historical count corresponding to the number of pasttimes that short-listed object X is selected as a short-listed object is1000, and the selection count corresponding to the number of times thata short-listed object A is selected as the final target object is 400.Thus, the support ratio for short-listed object A is 40%. In someembodiments, the server sends back only the short-listed objects withthe highest support ratio or one or several short-listed objects whosesupport ratios are ranked at the top.

Referring back to FIG. 3, the server calculates the support ratio for atleast one of the short-listed objects X, Y, Z and Q selected by thefirst user A and sends such data to the client so that the first user Amay read the data.

In some embodiment, the server determines the support information onthose short-listed objects with respect to their historical assistancerequest activities based on characteristic information of short-listedobjects. Thus, the server presents target object informationalrecommendations enabling the user to confirm the business informationreliability of short-listed objects based on reference data. Thisimproves the timeliness and reliability when acquiring business objectreliability and avoids communication resources waste, which occurs whenusers wait excessively long periods of time for feedback from otherusers.

In actual applications, because a large quantity of business objectsthat remain on Internet websites continuously for relatively longperiods of time exist, some information may already be out of date. Thefirst user may only wish to acquire historical selection information onshort-listed objects from a recent time interval. In this case, afterthe first user has selected short-listed objects on the client, the useralso sets a time range or selects a default recommended time rangeparameters set by the server. Subsequently, the client sends its targetobject informational recommendation request. Thus, the target objectrecommendation information request also includes the recommended timerange parameters set by the first user.

Therefore, in some embodiments, the server receives a target objectinformational recommendation request issued by the first user. Theserver then reads the recommended time range parameters set by the firstuser from the target object informational recommendation request. Theserver determines the time range corresponding to the recommended timerange parameters. Next, the server determines the historical selectioninformation for the plurality of short-listed objects within thedetermined time range.

In some embodiments, because the server saves the time whenever ashort-listed object is selected to be a short-listed object and the timewhenever the short-listed object is to be selected as a final targetobject, the server looks up, in the object information database, thehistorical selection information for the plurality of short-listedobjects within the time range corresponding to the recommended timerange parameters based on the time range parameters. The server thencalculates the short-listed object's support information, for example,support ratio, within the time range corresponding to the recommendedtime range parameters based on the historical selection information.

Using FIG. 3 as an example, the first user A wishes to acquire thehistorical selection information for the last month on a fewshort-listed objects that the first user A has selected. Thus, beforesending the target object informational recommendation request, thefirst user A selects the time range relating to the most recent month.The server, after receiving the first user A's request, screens theshort-listed object historical selection information so that theshort-listed object historical selection information includesinformation related to the most recent month.

In the implementation modes described above, avoiding data redundancy,to increase precision and timeliness of business object informationalrecommendations, and to increase querying efficiency is possible.

In some embodiments, since target object informational recommendationrequests require the expenditure of a large amount of network resources,the server becomes overloaded when some registered users continuallysend similar requests. In some embodiments, to avoid malicious requestsand to reduce server loads, limits are imposed on users who send therequests. FIG. 5 is a flowchart of another embodiment of a method forrecommending target object information. In some embodiments, the method500 is performed prior to determining the historical selectioninformation on the plurality of short-listed objects and comprises:

In 510, the server determines a reputation rank of the first user in auser database based on first user information.

In some embodiments, user ranks are determined according to the firstuser information and are recorded in user databases. The first userinformation includes user network behavior information. For example, theuser network behavior information includes: user registrationinformation, shopping information, access information, or anycombination thereof.

In some embodiments, the first user information includes the firstuser's ID information or other information.

In some embodiments, the determination of the reputation rank is basedon a user's review rating, whether a user has a VIP user registration,etc. For example, the user has a good review rating, a mixed reviewrating, a poor review rating, etc. The server sets the criteria fordetermining whether a user is qualified to make product recommendations,and rejects non-substantive recommendations to reduce the wasting ofresources.

In 520, the server determines whether the first user's reputation rankcomplies with rank restrictions that have been set.

In some embodiments, the server sets a rank threshold for users who canreply to target object informational recommendation requests. Forexample, referring back to FIG. 3, the threshold is set which is to bereached in order to send target object informational recommendationrequests.

Therefore, in some embodiments, prior to determining the historicalselection information for short-listed objects, the server usesinformation in a target object recommendation request to acquire statusinformation on the first user to assess whether the first user's rankmeets the rank restrictions that have been set. For example, the serverassesses whether the user's rank is above level 2. Thus, the serverreduces extra loads and also ensures the reliability of the providedhistorical selection information.

Subsequently, if the user that sent the target object informationalrecommendation request this time complies with the set rankrestrictions, the server determines the historical selection informationbased on the first user's request. If the user that sent the targetobject informational recommendation request this time does not complywith the set rank restrictions, the server rejects or stops thehistorical selection information query. Subsequently, the server queriesthe object information database for the historical selection informationof the short-listed objects selected by the first user to calculateobject support ratios.

In 530, in the event that the first user's reputation rank complies withthe set rank restriction, the server determines the historical selectioninformation on the plurality of short-listed objects of the first user.

For example, referring back to FIG. 3, the first user A is a user whoserank is above level 2. The server assesses that the first user Asatisfies the rank restriction that the server set. Subsequently, theserver begins to query the object information database for thehistorical selection information of the short-listed objects selected bythe first user A to calculate object support ratios. If user M sends atarget object informational recommendation request, and user M's rank islevel 1, the request will not meet the rank restrictions set by theserver. The server rejects user M's request, or the server does notprovide an option for sending target object informational recommendationrequests.

By using the above implementation modes, the server further reducesloads and also ensures reliability of provided data, and avoids theeffects of malicious requests on the historical selection information.

In another embodiments, the target object informational recommendationrequests received by the server include short-listed object attributeinformation, and prior to determining the historical selectioninformation on the plurality of short-listed objects, the server detectswhether attributes of the plurality of short-listed objects belong topreconfigured attribute standards. Examples of attribute standardsinclude categories, product types, brands, prices, and other suchinformation.

If the attributes of the plurality of short-listed objects belong to thepreconfigured attribute standards, the server performs the correspondinghistorical selection information queries. Otherwise, if the attributesof the plurality of short-listed objects do not belong to thepreconfigured attribute standards, the server queries only thehistorical selection information on one or more short-listed productsthat has the same attribute.

As an example, the server detects the attributes of the short-listedobjects selected by the first user. Typically, the attributes of theobjects that the first user chosen are the same. For example, assumingthat the target object that the first user wishes to select is a pair ofshoes. In that case, only products whose attribute is shoes have areference value. Therefore, the server assesses whether the plurality ofshort-listed objects that were selected have the same attribute andbelong to the same category. Thus, increasing the likelihood of validityof the historical selection information provided by the server andavoiding reduced data reference value, which results from comparisonsbetween products having different attributes, is possible. In someembodiments, based on the support information, the avoidance of areduced reference value means that the short-listed objects arecomparable unlike, for example, clothes and shoes which should not becompared.

Returning to the example, because what first user A wishes to select isa shoulder bag, only comparison data on the products that are shoulderbags is significant. Because of an error by the first user A, shoesmight also be added to the short-listed objects. At this point, theserver assesses whether products that are short-listed objects have thesame attribute based on short-listed object characteristic information,for example, product category and ID, included in the target objectbusiness recommendation request.

FIG. 6A is a flowchart of yet another embodiment of a method forrecommending target object information. In some embodiments, the method600 is performed prior to the server determining the historicalselection information on the plurality of short-listed objects andincludes:

In 610, the server determines the short-listed object attributeinformation based on short-listed object information.

As an example, the server looks up short-listed object attributes, suchas categories, product types, brands, prices, and other suchinformation, in the object information database based on theshort-listed object information.

In 620, the server groups the short-listed objects that are among theplurality of short-listed objects and that have partially or fully thesame attributes based on attribute information (e.g., categoryinformation) of the plurality of short-listed objects.

First users have different ways of selecting short-listed objects. Forexample, a first user selects many short-listed objects in batches bygoing through the first user's browsing history. Therefore, in someembodiments, the short-listed objects have different attributes, forexample, the short-listed objects belong to different categories.Referring back to FIG. 3, the first user A selects 50 short-listedobjects. These 50 short-listed objects are in categories includingshoes, pants, and hats. If the selected short-listed objects are notseparated, the historical selection information and support informationthat the user acquires will lack a reference value. Therefore, theshort-listed objects are to be divided into groups of short-listedobjects having the same category.

After the determining of the historical selection information on theplurality of short-listed objects, the server sends back according tothe groups the support information on the short-listed objects in eachgroup.

For example, following the division of the plurality of short-listedobjects into groups, the category corresponding to shoes contains 8short-listed objects, and the category corresponding to pants contains10 short-listed objects. Therefore, for the shoe category group, theserver sends back the support ratios for the one or two short-listedobjects that have higher ranked support ratios. For the pants categorygroup, the server sends back the support ratios for the one or twoshort-listed objects that have higher ranked support ratios. In someembodiments, the server sends back one or two short-listed objects, andthe first user finally selects a desired short-listed object. In someembodiments, the server sends back three short-listed objects, and thefirst user finally selects a desired short-listed object.

In addition, in some embodiments, the plurality of short-listed objectsselected by the first user belong to the same category, but in the caseof two objects of the short-listed objects belonging to the samecategory, a relatively large dissimilarity between the two objectsexists. For example, the shoes of a first-tier brand and the shoes of athird-tier brand belong to the same category, but are very dissimilar.FIG. 6B is a flowchart of yet another embodiment of a method forrecommending target object information. In some embodiments, the method650 is performed after the grouping of the short-listed objects that areamong the plurality of short-listed objects that have the sameattributes and comprises:

In some embodiments, the first two operations of method 650 correspondwith operations 610 and 620 of FIG. 6A and will not be further describedfor conciseness.

In 630, the server determines similarities of the plurality ofshort-listed objects within each group.

As an example, after the server divides into groups the plurality ofshort-listed objects based on the short-listed object attributeinformation, the server uses such attribute information as brand andprice to calculate the similarities of the short-listed objects havingthe same attribute. For example, first-tier brand shoes are verysimilar. In some embodiments, similarity is computed based on attributestandards. If differences between the attribute standards of theshort-listed objects are similar (for example, the objects are of thesame brand or have a similar price), then the short-listed objects aredeemed to be similar. Otherwise, the short-listed objects are deemed tobe dissimilar. In some embodiments, if the differences between theattribute standards of the short-listed objects are below apredetermined threshold, the attribute standards of the short-listedobjects are similar.

In 640, the server divides the groups of short-listed objects based onthe similarities of the short-listed objects within each group.

As an example, the server performs a secondary grouping of short-listedobjects that have the same attribute and that are highly similar basedon short-listed object similarity. For example, in the categorycorresponding to shoes, the server puts first-tier brand shoes into onesecondary group and puts second-tier brand shoes into another secondarygroup.

After the server determines the historical selection information on theplurality of short-listed objects, the server sends support informationon short-listed objects in each secondary group based on the secondarygroup division back to the client.

As an example, after the secondary grouping, the server sends back oneor more short-listed objects whose support ratios are ranked higher fromthe short-listed objects in each group.

Increasing the validity of historical selection information provided bythe server and avoiding a reduced data reference value which resultsfrom comparisons between products having different attributes ispossible, thus facilitating user selection and confirmation.

In some embodiments, the target object informational recommendationrequests received by the server include short-listed object linkaddresses. An example of a link address includes a URL. Therefore, priorto determining the historical selection information on the short-listedobjects, the server also detects whether link addresses of any of theshort-listed objects among the plurality of short-listed objects isvalid or not.

As an example, the short-listed objects selected by the first user mightbe selected from a browsing log. Therefore, in some embodiments, thelink addresses of the short-listed objects involve invalid conditions.For example, an invalid condition includes that the merchandise has beenremoved from the shelves or may not be in stock. Therefore, the servermonitors the link addresses of the short-listed objects to determinewhether the link addresses are valid. If a link address is invalid, theserver reports information on the invalidity of the link address to theclient. An example of the link address being invalid is a URL error. Theserver only determines the historical selection information onshort-listed objects with valid link addresses. Accordingly, historicalselection information on short-listed objects with invalid links doesnot have much reference value for the first user.

Referring back to FIG. 3, for example, when the user A adds ashort-listed object, he adds the short-listed object through a page, anaddress link, or a browsing log. In another example, the user A adds theshort-listed object using a bookmark folder or in some other way. Insome embodiments, when the request is sent, the short-listed object isalready in an unavailable state. Therefore, the server verifies thisinformation. The server no longer provides historical selectioninformation for products that were removed from the shelves or no longeron sale, and the server discontinues informational recommendationprocessing.

Therefore, the above approach increases the precision of business objectreliability assessments.

In some embodiments, the target object informational recommendationrequests received by the server include information, for example,identifier (ID), on the plurality of short-listed objects. FIG. 7 is aflowchart of yet another embodiment of a method for recommending targetobject information. In some embodiments, the method 700 is performedprior to the determining of historical selection information on theplurality of short-listed objects and comprises:

In 710, the server determines providing object information on theshort-listed objects based on the short-listed object information. Forexample, the providing object information includes photos of theshort-listed objects, sellers of the short-listed objects, productdescriptions of the short-listed objects, URLs of the short-listedobjects, or any combination thereof.

In some embodiments, the information relating to the providing objectrelates to a reputation standard. As an example, if the first user paysattention only to products from sellers with higher reputations, thefirst user selects the providing object standard, for example, theproviding object reputation standard, for any short-listed object on theclient page. For example, attention is paid only to the historicalselection information on products provided by high-level providers.Therefore, the target object informational recommendation request sentby the first user has attached to the request a reputation standardselected and set by the first user. The server looks up rank informationon providing objects for short-listed objects in the object informationdatabase based on short-listed object IDs included in the target objectinformational recommendation requests.

In 720, the server detects whether the providing object of any of theplurality of short-listed objects meets a set standard.

As an example, after the server determines a reputation level for theproviding object on the short-listed objects, the server compares thereputation level to the reputation standard requirements set by thefirst user. If the provider of the short-listed object does not meet thereputation standard set by the first user through the client, thehistorical selection information on the short-listed object is deleted.In some embodiments, only the historical selection information onshort-listed objects that meet the set reputation standard is provided.

For example, in order to assure the quality of products, the first userA wishes only to select products provided by sellers with relativelyhigh reputations. Therefore, the first user A pays attention only tohistorical selection information on products provided by high-reputationsellers. Therefore, before sending the target object recommendationrequest, the first user A selects a setting for the seller reputationstandard range on the web page. For example, if the setting isroyal-class seller, the server, after receiving the request from firstuser A, conducts automatic screening and does not count historicalselection information on products provided by merchants whose sellerreputation fails to attain the royal level. In some embodiments, theroyal class or the royal level is the highest level of sellerreputation.

Based on the above method, avoiding information that does not interestthe querying user is possible. The above method also increases queryingefficiency and conserves communication resources.

The several above optional implementation modes described may beselected and executed in any order and in any combination. The sequencedescribed above merely served to facilitate the description and shouldnot be interpreted as a limitation on the present application.

Through the above embodiments, the server acquires historical selectioninformation on the short-listed objects for the reference of the firstuser in confirming the reliability of the short-listed objects and forselection therefrom of target objects. Thus, the server acquires andrecords the target objects that the first user, based on the historicalselection information on the plurality of short-listed objects, selectsfrom the plurality of short-listed objects, to update the historicalselection information on these short-listed objects, and to provide thehistorical selection information on these short-listed objects for thereference of the next first user.

FIG. 8 is a flowchart of yet another embodiment of a method forrecommending target object information. In some embodiments, the method800 is implemented by the server 110 of FIG. 1 and comprises:

In 810, the server queries fourth users. The fourth users have aconnection to the short-listed objects. The fourth users have made atleast one of the short-listed objects a final target object.

As an example, the server determines, from the object informationdatabase, the users who purchased or used a certain short-listed objectas the fourth users.

In 820, the server sends invitation information to the fourth users, theinvitation information being for inviting the fourth users to carry outtarget object informational recommendations regarding the short-listedobjects of the first user.

As an example, the server sends to one or more fourth users that it haslooked up invitation messages inviting the fourth users to evaluate,vote on, or grade short-listed objects that the fourth users have used.

In 830, the server records recommendation information of the fourthusers on the short-listed objects of the first user, and takes therecommendation information of the fourth users regarding theshort-listed objects as support information for the short-listedobjects.

As an example, the server regards the recommendation information, suchas ratings, on a product that is a short-listed object as supportinformation on the short-listed object, and sends the recommendationinformation to a client for the reference of the user.

In some embodiments, the server not only sends the historical supportinformation on the short-listed objects back to the first user forreference of the first user, but the server also sends short-listedobject evaluation information and uses information from users who usedor purchased the short-listed objects to a client for the reference ofthe user.

FIG. 9 is a flowchart of yet another embodiment of a method forrecommending target object information. In some embodiments, the method900 is implemented by the client 120 of FIG. 1 and comprises:

In 910, the client receives information on a plurality of short-listedobjects selected by a first user.

As an example, clients include smart phones, tablet computers, personalcomputers, etc. The first user visits an Internet website via theclient, browses business objects displayed on the corresponding webpages of the website, and selects business objects to determine thefinal target objects. For example, the product objects displayed on thecorresponding web pages of the website are business objects, forexample, physical products, virtual products, valuable information, etc.The final target objects are confirmed by browsing the business objectson the corresponding web pages and by selecting business objects. Thefirst user selects a plurality of business objects as short-listedobjects based on the business objects displayed on web pages on theclient.

The ways in which the first user selects short-listed objects includeadding short-listed objects via bookmark records in a bookmark folder,adding short-listed objects via a browsing log in historical browsingrecords, adding short-listed objects by entering short-listed objectlink addresses, etc.

Referring back to FIG. 3, the server 310 is an e-commerce platform, suchas Taobao or Tmall, providing online shopping. The user accesses thebusiness platform server 310 via the client 330 and selects productsthat the user wants to purchase from the products displayed on thebusiness platform server 310.

After user A, who is a first user, logs onto the e-commerce platformthrough the web page of client 330, as shown in FIG. 4, the user A makesselections from the plurality of products provided by the server 310 ofthe commercial platform. With the first user A having determined aplurality of short-listed objects within the same category of products,the first user A selects one or more of the plurality of short-listedobjects as products for final purchase and issues an order. Because theshort-listed objects are before the first user A, the first user A isfaced with many choices. Thus, the first user A desires that the server310 provides informational recommendations concerning products that areshort-listed objects.

For example, the commercial platform server 310 provides an applicationthat solicits recommendations from many users. After first user Abrowses Taobao, the first user A wishes to purchase a shoulder bag.After browsing the server 310, the first user A selects 6 or 7short-list products, yet the first user A finds it very difficult tochoose between some of the products on the short list. First of all, theuser selects products that require the addition of informationalrecommendations, these products being the short-listed objects. Whenselecting information-recommended products, the first user A selectsbrowsed products from the browser records, selects bookmarked productsfrom the bookmark folder, selects products from shopping records,directly enters a product link in a prompt field, or any combinationthereof. In some embodiments, the user selects products having theaddition of informational recommendations.

For example, referring back to FIG. 4, four products X, Y, Z and Qexist. The first user A desires that the server 310 of the businessplatform invites a plurality of users to give informationalrecommendations on the four products X, Y, Z and Q.

Referring back to FIG. 9, in 920, the client generates target objectinformational recommendation requests based on the received information,the requests including information on the plurality of short-listedobjects selected by the first user.

As an example, after the first user selects the plurality ofshort-listed objects on the client, the client generates the targetobject informational recommendation requests based on the selectedinformation. In some embodiments, the target object informationalrecommendation requests include characteristic information on theshort-listed objects selected by the first user. Examples of thecharacteristic information include one or more of the following: thecategory to which the short-listed object belongs, name of theshort-listed object, number, ID, link address URLs, and otherinformation.

For example, after the first user A selects the short-listed objects X,Y, Z and Q via the browser interface of the client 330, the first user Aclicks an interface control on the client 330 triggering the client togenerate a target object informational recommendation request. Thetarget object recommendation request that is generated includesinformation on the short-listed objects X, Y, Z and Q.

In 930, the client sends the target object informational recommendationrequest to the server.

As an example, after the client generates the target objectinformational recommendation request for the short-listed objectsselected by the first user, the client sends the target objectinformational recommendation request via the Internet or the network 320to the server. The server provides historical selection informationfeedback for the short-listed objects.

In 940, the client receives part or all of the short-listed objecthistorical selection information and/or support information, sent by theserver. In some embodiments, the historical selection informationincludes the historical count, i.e., the number of past times that eachshort-listed object among the plurality of short-listed objects wasselected by second users with the target object informationalrecommendation request being sent to a server and the selection count,i.e., the number of times that said short-listed objects among targetobject informational recommendation requests (whose quantity is thehistorical count) were selected by the second users as target objects.The support information is support information that refers to thehistorical count and the selection count and is acquired for at leastone short-listed object among the short-listed objects.

In some embodiments, the first user makes selections based on thehistorical count for the short-listed objects, the selection count, thesupport information, or any combination thereof.

As an example, when the client receives the historical count and theselection count, the client calculates the support information from thehistorical count and the selection count. Thus, support information onone or more of the short-listed objects sent back by the server isreceived. In some embodiments, the support information includes ahistorical support ratio for the short-listed objects and evaluationinformation on the short-listed objects. The support ratio is calculatedin the same way as described above and thus the calculation is omittedfor conciseness.

Referring back to FIG. 3, after the server receives the target objectinformational recommendation request sent by the client, the serverlooks up the historical count, i.e., the number of past times that theshort-listed objects X, Y, Z and Q were selected as short-listed objectsand the selection count, i.e., the number of times that they wereultimately selected as target objects and thereby obtains the supportratio for each short-listed object. Assume that the support ratio for Xis 30%, the support ratio for Y is 50%, the support ratio for Z is 18%,and the support ratio for Q is 40%. Therefore, in some embodiments, theserver chooses to send back support information only for theshort-listed object Y, which has the highest support ratio andrecommends Y to the first user. The client only displays the supportinformation for Y. In some embodiments, the server sends back thesupport ratios for short-listed objects Y and Q, whose support ratiosare the two highest-ranked, to the client. The client displays thesupport ratios for Y and Q and recommends both to the user A.

In some embodiments, the support ratio is calculated by the server. Theserver only sends back the number of past times that short-listedobjects were selected as short-listed objects (the historical count) andthe number of times that the short-listed objects were selected astarget objects (the selection count) to the client. The clientcalculates the support ratio for each short-listed object.

The method 900 enables the user to confirm product information, and isable to conserve communication resources.

FIG. 10 is a structural diagram of an embodiment of a server forrecommending target object information. In some embodiments, the server1000 comprises a receiving unit 1010, a determining unit 1020, and asending unit 1030.

The receiving unit 1010 receives target object informationalrecommendation requests, the requests including information on aplurality of short-listed objects selected by a first user.

The determining unit 1020 determine historical selection information onthe plurality of short-listed objects, the historical selectioninformation including a historical count being the number of past timesthat each short-listed object among the plurality of short-listedobjects was selected by second users with the target objectinformational recommendation request being sent to a server and/or theselection count being the number of times that the short-listed objectsamong the target object informational recommendation requests (whosequantity is the historical count) were selected by the second users astarget objects.

The sending unit 1030 takes part or all of the plurality of short-listedobject historical selection information or support information obtainedby referencing historical selection information and sends the historicalselection information or the support information to a client.

As an example, when the first user has the plurality of short-listedobjects available to the first user through various browsers, such aspersonal computers (PCs), smart phones, tablet computers, and otherclients, the first user sends a target object informationalrecommendation request by triggering a control of the client browser orapplication interface. The receiving unit 1010 receives the targetobject informational recommendation requests and at the same timeacquires characteristic information, such as categories and names, onthe plurality of short-listed objects within a target objectinformational recommendation request.

The determining unit 1020 looks up and determines the historicalselection information on each short-listed object in the objectinformation database based on the content of the target objectinformational recommendation request and the acquired characteristicinformation on the short-listed objects. In other words, the number ofpast times that the objects were selected as short-listed objects (thehistorical count), the number of times that the objects were selected toparticipate in helping with choices, the number of times that the aboveshort-listed objects were selected as final target objects (theselection count), and other such data.

After the determining unit 1020 acquires the historical selectioninformation on the acquired short-listed objects, the sending unit 1030sends the historical selection information or the support information(that was acquired with reference to the historical selectioninformation) to a client. The acquired information is displayed via theclient for user reference.

FIG. 11 is a structural diagram of an embodiment of a client forrecommending target object information. In some embodiments, the client1100 includes a first receiving unit 1110, a generating unit 1120, asending unit 1130, and a second receiving unit 1140.

The first receiving unit 1110 receives information selected by a firstuser on a plurality of short-listed objects.

The generating unit 1120 generates target object informationalrecommendation requests based on the received information, the targetobject informational recommendation requests including information onthe plurality of short-listed objects selected by the first user.

The sending unit 1130 determines historical selection information on theplurality of short-listed objects based on the target objectrecommendation requests and sends the target object informationalrecommendation requests to a server, the historical selectioninformation including a historical count, the historical count being thenumber of past times that each short-listed object was selected from theplurality of short-listed objects by second users with the target objectinformation recommending request being sent to a server and/or theselection count, the selection count being the number of times that theshort-listed objects were selected from past target object informationalrecommendation requests (whose quantity is said historical count) by thesecond users as target objects.

The second receiving unit 1140 receives part or all of the short-listedobject historical selection information or support information sent bythe server, the support information referring to the number of pasttimes and the number of selections and being support information for atleast one short-listed object acquired from the short-listed objects.

The units described above can be implemented as software componentsexecuting on one or more general purpose processors, as hardware such asprogrammable logic devices and/or Application Specific IntegratedCircuits designed to perform certain functions or a combination thereof.In some embodiments, the units can be embodied by a form of softwareproducts which can be stored in a nonvolatile storage medium (such asoptical disk, flash storage device, mobile hard disk, etc.), including anumber of instructions for making a computer device (such as personalcomputers, servers, network equipment, etc.) implement the methodsdescribed in the embodiments of the present invention. The units may beimplemented on a single device or distributed across multiple devices.The functions of the units may be merged into one another or furthersplit into multiple sub-units.

The methods or algorithmic steps described in light of the embodimentsdisclosed herein can be implemented using hardware, processor-executedsoftware modules, or combinations of both. Software modules can beinstalled in random-access memory (RAM), memory, read-only memory (ROM),electrically programmable ROM, electrically erasable programmable ROM,registers, hard drives, removable disks, CD-ROM, or any other forms ofstorage media known in the technical field.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method for recommending target objectinformation, comprising: receiving a target object informationalrecommendation request, the target object informational recommendationrequest including information pertaining to a plurality of short-listedobjects selected by a first user; determining historical selectioninformation on the plurality of short-listed objects, the historicalselection information including, historical counts and selection counts,the historical counts corresponding to number of past times that eachshort-listed object was selected with past target object informationrecommending request being sent to a server, and the selection countscorresponding to number of times that each short-listed object wasselected from past target object informational recommendation requestsby second users as a target object; and sending a part or all of theshort-listed objects' historical selection information to a client,wherein the sending of the part or all of the short-listed objects'historical selection information to the client comprises: calculatingsupport ratios for the plurality of short-listed objects based on theselection counts and the historical counts of the plurality ofshort-listed objects; ranking the support ratios of the plurality ofshort-listed objects; selecting one or more short-listed objects basedon the ranked support ratios; and sending the selected one or moreshort-listed objects to the client.
 2. The method as described in claim1, wherein the historical selection information comprises evaluationinformation by the second users on the short-listed objects.
 3. Themethod as described in claim 1, wherein: the target object informationalrecommendation request includes recommended time range parameters; andthe determining of the historical selection information on the pluralityof short-listed objects comprising: determining a time rangecorresponding to the recommended time range parameters; and determiningthe historical selection information on the plurality of short-listedobjects within the time range.
 4. The method as described in claim 1,wherein the target object informational recommendation requests includefirst user information; and further comprising: prior to the determiningof the historical selection information on the plurality of short-listedobjects: looking up a rank of the first user in a user database based onfirst user information; assessing whether the rank of the first usercomplies with rank restrictions that have been set; and in the eventthat the rank of the first user complies with the rank restrictions thathave been set, executing the determining of the historical selectioninformation on the plurality of short-listed objects of the first user.5. The method as described in claim 1, wherein the target objectinformational recommendation request includes attribute information onthe plurality of short-listed objects; and wherein the method furthercomprises: prior to the determining of the historical selectioninformation on the plurality of short-listed objects, detecting whetherattributes of the plurality of short-listed objects belong to a set ofpreconfigured attribute standards, wherein the determining of thehistorical selection information on the plurality of short-listedobjects comprises determining the historical selection information onthe short-listed objects that, among the plurality of short-listedobjects, have attributes that belong to the set of preconfiguredattribute standards.
 6. The method as described in claim 1, furthercomprising: prior to the determining of the historical selectioninformation on the short-listed objects: determining attributeinformation of the plurality of short-listed object based onshort-listed object information; grouping short-listed objects that areamong the plurality of short-listed objects and that have the sameattributes based on the attribute information on the plurality ofshort-listed objects; and after the determining of the historicalselection information on the plurality of short-listed objects, sendingback, in accordance with group divisions, support information onshort-listed objects of each group.
 7. The method as described in claim6, further comprising: after the grouping of the short-listed objectsthat are among the plurality of short-listed objects and that have thesame attributes: determining similarities of the plurality ofshort-listed objects within each group; dividing the plurality ofshort-listed objects within each group into second-division groups basedon the similarities of the plurality of short-listed objects within eachgroup; and after the determining of the historical selection informationon the plurality of short-listed objects, sending support information onshort-listed objects of each of the second-division groups back to theclient.
 8. The method as described in claim 6, wherein the supportinformation is a support ratio of the short-listed objects, the sendingof the support information on at least one of the plurality ofshort-listed objects to a client comprises: determining whether ashort-listed object has a higher support ratio than a preset threshold,wherein the support ratio is based on the historical count and theselection count; and sending the support information on one or more ofthe short-listed objects with support ratios higher than the presetthreshold to a client.
 9. The method as described in claim 1, whereinthe target object informational recommendation requests include linkaddresses for the plurality of short-listed objects; and furthercomprising: prior to the determining of the historical selectioninformation on the plurality of short-listed objects, detecting whethera link address of a short-listed object is valid, wherein thedetermining of the historical selection information on the plurality ofshort-listed objects comprises: determining the historical selectioninformation on short-listed objects with valid link addresses.
 10. Themethod as described in claim 1, wherein the target object informationalrecommendation requests includes information on the plurality ofshort-listed objects; and wherein the method further comprises: prior tothe determining of the historical selection information on the pluralityof short-listed objects: determining information on a providing objectof the short-listed objects based on the short-listed objects'information; and detecting whether the providing object of any one ofthe plurality of short-listed objects meeting a preconfigured standardbased on the short-listed object' information, wherein the determiningof the historical selection information on the plurality of short-listedobjects includes determining the historical selection information onshort-listed objects whose providing objects meet set standards.
 11. Themethod as described in claim 6, further comprising: after sending thesupport information on at least one of the plurality of short-listedobjects to a client: recording the target object selected by the firstuser from among the plurality of short-listed objects according to thehistorical selection information on the plurality of short-listedobjects; and regarding selection made this time by the first user as oneinstance of historical selection information on the target object. 12.The method as described in claim 1, further comprising: after thereceiving of target object informational recommendation request, therequest including the information on the plurality of short-listedobjects selected by the first user: determining third users of theserver; issuing participation requests to the third users to invite thethird users to make target object informational recommendationsregarding short-listed objects of the first user; recordingrecommendation information of the third users on the short-listedobjects of the first user; and taking the recommendation information ofthe third users on the short-listed objects as one instance ofhistorical selection information on the short-listed objects.
 13. Themethod as described in claim 1, further comprising: sending invitationinformation to fourth users to invite the fourth users to perform targetobject informational recommendations regarding the short-listed objectsof the first user; recording recommendation information of the fourthusers on the short-listed objects of the first user; and taking therecommendation information of the fourth users regarding theshort-listed objects as support information for the short-listedobjects.
 14. The method as described in claim 13, wherein the fourthusers are users associated with the short-listed objects, the fourthusers being users who have selected at least one of the plurality ofshort-listed objects as the target object.
 15. The method as describedin claim 1, wherein the selecting one or more short-listed objects basedon the ranked support ratios comprising selecting N short-listed objectsbased on the one or more short-listed objects having the N highestsupport ratios, N being an integer.
 16. A method for recommending targetobject information, comprising: receiving information on a plurality ofshort-listed objects selected by a first user; generating a targetobject informational recommendation request based on selectedinformation, the target object informational recommendation requestincluding information on the plurality of short-listed objects selectedby the first user; sending the target object informationalrecommendation requests to a server configured to determine historicalselection information on the plurality of short-listed objects based onthe target object informational recommendation request, the historicalselection information including historical counts that correspond tonumber of past times that the plurality of short-listed objects wereselected with a target object information recommending request beingsent to a server and selection counts that correspond to number of timesthat the plurality of short-listed objects were selected from pasttarget object informational recommendation requests by second users as atarget object; and receiving at least some of the short-listed objects'historical selection information, sent by the server, comprising:calculating support ratios for at least some short-listed objects basedon selection counts and historical counts of the at least someshort-listed objects; ranking the support ratios of the at least someshort-listed objects; and selecting one or more short-listed objectsbased on the ranked support ratios.
 17. The method as described in claim16, wherein: the receiving of the information on the plurality ofshort-listed objects selected by the first user comprises: receivingshort-listed objects selected by the first user through a bookmarkfolder or historical browsing records, the received informationincluding the short-listed object information; and the target objectinformational recommendation request includes information on theplurality of short-listed objects selected by the first user.
 18. Themethod as described in claim 16, wherein: the receiving of informationselected by the first user on the plurality of short-listed objectsincludes: receiving short-listed objects selected by the first userthrough link addresses, the received information including the linkaddresses on the short-listed objects; and the target objectinformational recommendation request including the link addresses forthe plurality of short-listed objects selected by the first user. 19.The method as described in claim 16, wherein the support ratio includessupport information for at least one short-listed object acquired fromthe short-listed objects with reference to the number of past times andthe number of selections.
 20. A server for recommending target objectinformation, comprising: at least one processor configured to: receive atarget object informational recommendation request, the target objectinformational recommendation request including information pertaining toa plurality of short-listed objects selected by a first user; determinehistorical selection information on the plurality of short-listedobjects, the historical selection information including historicalcounts and selection counts, the historical counts corresponding tonumber of past times that each short-listed object was selected withpast target object information recommending request being sent to aserver, and the selection counts corresponding to number of times thateach short-listed object was selected from past target objectinformational recommendation requests by second users as a targetobject; and send a part or all of the short-listed objects' historicalselection information to a client, wherein the sending of the part orall of the short-listed objects' historical selection information to theclient comprises to: calculate support ratios for the plurality ofshort-listed objects based on the selection counts and the historicalcounts of the plurality of short-listed objects; rank the support ratiosof the plurality of short-listed objects; select one or moreshort-listed objects based on the ranked support ratios; and send theselected one or more short-listed objects to the client; and a memorycoupled to the at least one processor and configured to provide the atleast one processor with instructions.
 21. A client for recommendingtarget object information, comprising: at least one processor configuredto: receive information on a plurality of short-listed objects selectedby a first user; generate target object informational recommendationrequests based on selected information, the target object informationalrecommendation requests including information pertaining to theplurality of short-listed objects selected by the first user; send thetarget object informational recommendation requests to a server so thatthe server determines historical selection information on the pluralityof short-listed objects based on the target object informationalrecommendation requests, the historical selection information includinghistorical countsthat correspond to number of past times that theplurality of short-listed objects were selected with a past targetobject information recommending request being sent to a server andselection counts that correspond to number of times that the pluralityof short-listed objects were selected from past target objectinformational recommendation requests by second users as a targetobject; and receive part or all of the short-listed objects' historicalselection information sent by the server, comprising to: calculatesupport ratios for at least some short-listed objects based on selectioncounts and historical counts of the at least some short-listed objects;rank the support ratios of the at least some short-listed objects; andselect one or more short-listed objects based on the ranked supportratios; and a memory coupled to the at least one processor andconfigured to provide the at least one processor with instructions. 22.A computer program product for recommending target object information,the computer program product being embodied in a tangible non-transitorycomputer readable storage medium and comprising computer instructionsfor: receiving a target object informational recommendation request, thetarget object informational recommendation request including informationpertaining to a plurality of short-listed objects selected by a firstuser; determining historical selection information on the plurality ofshort-listed objects, the historical selection information includinghistorical counts and selection counts, the historical countscorresponding to number of past times that each short-listed object wasselected with a past target object information recommending requestbeing sent to a server, and the selection counts corresponding to numberof times that each short-listed object was selected from past targetobject informational recommendation requests by second users as a targetobject; and sending a part or all of the short-listed objects'historical selection information to a client, wherein the sending of thepart or all of the short-listed objects' historical selectioninformation to the client comprises: calculating support ratios for theplurality of short-listed objects based on the selection counts and thehistorical counts of the plurality of short-listed objects; ranking thesupport ratios of the plurality of short-listed objects; selecting oneor more short-listed objects based on the ranked support ratios; andsending the selected one or more short-listed objects to the client. 23.A computer program product for recommending target object information,the computer program product being embodied in a tangible non-transitorycomputer readable storage medium and comprising computer instructionsfor: receiving information on a plurality of short-listed objectsselected by a first user; generating target object informationalrecommendation requests based on selected information, the target objectinformational recommendation requests including information pertainingto the plurality of short-listed objects selected by the first user;sending the target object informational recommendation requests to aserver so that the server determines historical selection information onthe plurality of short-listed objects based on the target objectrecommendation requests, the historical selection information includinghistorical counts that correspond to number of past times that theplurality of short-listed objects were selected with a past targetobject information recommending request being sent to a server andselection counts that correspond to number of times that the pluralityof short-listed objects were selected from past target objectinformational recommendation requests by second users as a targetobject; and receiving part or all of the short-listed objects'historical selection information sent by the server, comprising:calculating support ratios for at least some short-listed objects basedon selection counts and historical counts of the at least someshort-listed objects; ranking the support ratios of the at least someshort-listed objects; and selecting one or more short-listed objectsbased on the ranked support ratios.